| name | codexkit-data-quality-auditor |
| description | Audit data quality across six DAMA DMBOK dimensions — Completeness, Accuracy, Consistency, Timeliness, Validity, Uniqueness. Produces a scored data quality report with issue log and remediation plan. Use when profiling datasets, onboarding new data sources, or building data quality gates. |
| version | 1.0.0 |
| category | verification |
Data Quality Auditor
When to Use
- Before building analytics or dashboards on a new data source
- When data issues cause downstream report errors
- During data migration or system integration
- When establishing data quality monitoring rules
Procedure
Step 1 — Scope & Profiling
Identify the dataset and context:
- Table/file name, row count, column count
- Business purpose: what decisions does this data support?
- Data owner: who is accountable?
Profile the data:
- Column types (string, numeric, date, boolean, null)
- Null rates per column
- Distinct value counts
- Min/Max/Mean for numerics
- Date range coverage
Step 2 — Six-Dimension Assessment
Score each dimension 1–5:
| Dimension | Question | Score Method |
|---|
| Completeness | Are all required fields populated? | % non-null for required fields |
| Accuracy | Do values represent reality? | Sample validation against source |
| Consistency | Do related fields agree? | Cross-field rule checks |
| Timeliness | Is data current enough for its purpose? | Max staleness vs requirement |
| Validity | Do values conform to allowed ranges/formats? | Format + range validation |
| Uniqueness | Are there unwanted duplicates? | Duplicate rate on key columns |
Step 3 — Issue Log
For each issue found:
| # | Dimension | Column(s) | Issue Description | Severity | Records Affected | Example |
|---|
| 1 | Completeness | email | 12% null in required field | High | 1,200 | row 45: null |
Severity: Critical (blocks use) / High (degrades quality) / Medium (cosmetic) / Low (nice-to-fix)
Step 4 — Remediation Plan
For each Critical/High issue:
| Issue | Root Cause | Fix | Owner | Deadline |
|---|
| Missing emails | Optional in old form | Backfill from CRM | Data Eng | Sprint 4 |
Step 5 — Monitoring Rules
Define ongoing quality checks:
- Automated checks to run on each data load
- Alerting thresholds (if quality drops below X%, alert owner)
- Review cadence (weekly, monthly)
Inputs
| Input | Required | Format |
|---|
| Dataset or table | Yes | Name + access method |
| Business context | Yes | What this data is used for |
| Quality rules / expectations | Recommended | Any existing rules or SLAs |
| Sample size | Recommended | Full scan or % sample |
Output
## Data Quality Report — [Dataset Name]
### Summary
- **Dataset:** orders_2024
- **Rows:** 145,000 | **Columns:** 23
- **Overall Quality Score:** 3.8 / 5.0
### Dimension Scores
| Dimension | Score | Key Finding |
|-----------|-------|-------------|
| Completeness | 4/5 | 2 columns with >5% nulls |
| Accuracy | 3/5 | 3% of prices don't match catalog |
| Consistency | 4/5 | state vs zip mismatch in 1.2% |
| Timeliness | 5/5 | Data refreshed within SLA |
| Validity | 3/5 | 8% of emails fail format check |
| Uniqueness | 4/5 | 0.5% duplicate order IDs |
### Critical Issues
[Issue log table]
### Remediation Plan
[Fix plan with owners and deadlines]
### Monitoring Rules
[Automated checks to add]
Definition of Done
Quality Criteria
Verification (4C)
| Check | Question |
|---|
| Correctness | Are all pass/fail criteria applied against the correct standard or rule? |
| Completeness | Were all required dimensions or checklist items evaluated? |
| Context-fit | Does the verification scope match the actual risk level of the deliverable? |
| Consequence | If this passed verification but had a hidden flaw, what is the worst-case impact? |
Edge Cases
- Incomplete data for full assessment — Document which checks were limited and flag for re-verification when data becomes available.
- Ambiguous pass/fail criteria — Request clarification from the standard owner before scoring. Mark as 'Needs Review'.
- Multiple overlapping standards — Identify the governing standard and note where others diverge.
Changelog
